CN111563384B - Evaluation object identification method and device for E-commerce products and storage medium - Google Patents
Evaluation object identification method and device for E-commerce products and storage medium Download PDFInfo
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Abstract
The invention discloses an evaluation object identification method, device and storage medium for electronic commerce products, wherein the method comprises the following steps: pairwise forming word pairs by candidate evaluation objects and candidate evaluation words in the same sentence; performing association degree calculation on the word pairs by adopting a word pair association degree model, and acquiring a word pair set according to a calculation result; performing iterative identification on the word pair set to obtain a candidate evaluation object set and a candidate evaluation word set; dividing candidate evaluation objects in the candidate evaluation object set according to the word frequency to obtain a first candidate evaluation object set and a second candidate evaluation object set; and acquiring an accurate evaluation object from the second candidate evaluation object set, and acquiring an accurate evaluation object set by combining the first candidate evaluation object set and the accurate evaluation object. In the recall strategy of the evaluation objects, the semantic similarity between the evaluation objects is measured by using the similarity of the word mixed vector, so that the recall rate and the accuracy of the evaluation objects are improved, and the method can be applied to the field of opinion mining.
Description
Technical Field
The invention relates to the field of opinion mining, in particular to an evaluation object identification method and device for E-commerce products and a storage medium.
Background
With the rapid rise of the internet, the e-commerce product platform also has a rapid development, and the e-commerce product platform known by the public at present has treasure, jingdong, shuduo and Dang, and the like. The rapid development of the e-commerce platform means that e-commerce products also become more diversified and the content of the e-commerce products is also richer. In the face of diversified and rich e-commerce products, consumers usually want to know corresponding e-commerce products from the side of a platform, the most prominent side information is the online comment of the e-commerce products, and the consumers can objectively obtain all dimension information of the products through comment information of other consumers. As the merchant of platform use, they can improve the product or make the sales strategy according to the comment information that the customer feedbacks. Therefore, information processing for product reviews of e-commerce platforms is a very popular research area.
The product comment information of the e-commerce platform has the characteristics of large quantity and inconsistent data format standards, and the traditional processing method has a manual review method, which consumes a large amount of manpower and time and has poor obtained effect. There are also methods of using customized rules of dependency syntax relationships that do not take into account text semantic information and part-of-speech feature information of word order. At this time, it is necessary to combine some deep learning algorithms to help human beings process the huge product comment information, and quickly arrange the human-understandable structured information, which is also the purpose of opinion mining technology.
The opinion mining technology mainly takes natural language processing, information extraction, data mining and the like as means to identify and extract valuable viewpoint information from a large amount of text information. One important task is the extraction of evaluation objects for e-commerce products. The existing method for extracting the evaluation objects is lack of a method with high accuracy and high recall rate.
Disclosure of Invention
In order to solve one of the above technical problems, an object of the present invention is to provide a method, an apparatus, and a storage medium for identifying an evaluation object for an e-commerce product.
The invention adopts a technical scheme that:
an evaluation object identification method for E-commerce products comprises the following steps:
acquiring a first comment corpus, extracting candidate evaluation objects and candidate evaluation words from sentences of the first comment corpus according to preset parts of speech, and pairwise forming word pairs by the candidate evaluation objects and the candidate evaluation words in the same sentence;
performing association degree calculation on the word pairs by adopting the trained word pair association degree model, and acquiring a word pair set according to a calculation result;
performing iterative recognition on the word pair set by adopting a preset recognition algorithm to obtain a candidate evaluation object set and a candidate evaluation word set;
dividing candidate evaluation objects in the candidate evaluation object set according to word frequency to obtain a first candidate evaluation object set and a second candidate evaluation object set, wherein the word frequency is the frequency of the candidate evaluation objects or the candidate evaluation words appearing in the word pair set;
and combining the word mixed vector and the first candidate evaluation object set, obtaining an accurate evaluation object from the second candidate evaluation object set, and combining the first candidate evaluation object set and the accurate evaluation object to obtain an accurate evaluation object set, wherein the word mixed vector is a combination of a word vector and a word vector.
Further, the method also comprises a step of training the word pair association degree model, which specifically comprises the following steps:
acquiring a second comment corpus, and performing text preprocessing and labeling on the second comment corpus to obtain a training data set;
and (5) establishing a word pair association degree model, and training the word pair association degree model by adopting a training data set.
Further, the obtaining a second comment corpus, performing text preprocessing and labeling on the second comment corpus, and obtaining a training data set includes:
acquiring a second comment corpus, and preprocessing the second comment corpus to obtain a plurality of comment sentences, wherein the preprocessing comprises word segmentation and part-of-speech tagging of the sentences in the second comment corpus;
marking out an evaluation object and an evaluation word in a comment sentence in a manual mode, and combining the evaluation object and the evaluation word in pairs to serve as a word pair;
constructing a sentence pair of < original sentence, word pair >, marking whether the word pair has an evaluation collocation relationship, and if the evaluation collocation relationship exists, marking the class as a first type; otherwise, the class is marked as a second type;
and forming a training data set by the marked comment sentences, wherein the format of the marked comment sentences is < original sentence, evaluation word pair, and corresponding part-of-speech sequence, category > after the original sentence is segmented.
Further, the word pair association model is based on BERT-CNN, and includes an input layer, an embedding layer, a coding layer and a classification decoding layer, and the training of the word pair association model by using a training data set includes:
fine tuning parameters of the coding layer by adopting a training data set;
and training the word pair association degree model by adopting a cross entropy function as a target function to obtain a final word pair association degree model.
Further, the preset recognition algorithm includes a first formula and a second formula, and the performing iterative recognition on the word pair set by using the preset recognition algorithm to obtain a candidate evaluation object set and a candidate evaluation word set includes:
initializing a candidate evaluation object set and a candidate evaluation word set to be null;
acquiring an evaluation word from the word pair set, determining that the evaluation word belongs to the seed emotion word, and inputting the evaluation word into the candidate evaluation word set;
acquiring a candidate evaluation object from the word pair set, calculating a first association degree by combining the candidate evaluation object, the candidate evaluation word set and a first formula, determining that the first association degree is greater than a first threshold value, and inputting the candidate evaluation object into the candidate evaluation object set;
and acquiring candidate evaluation words from the word pair set, calculating a second association degree by combining the candidate evaluation words, the candidate evaluation object set and a second formula, determining that the second association degree is greater than a second threshold value, and inputting the candidate evaluation words into the candidate evaluation word set.
Further, the first formula is:
wherein rel i As candidate evaluation object o i The degree of association with the entire set of candidate evaluation words,<o i ,s j >is a candidate word pair, #<o i ,s j >Frequency of occurrence of candidate word pairs, sim: (<o i ,s j >) A degree of association calculated for the degree of association model;
the second formula is:
wherein rel j As candidate evaluation words s j The degree of association with the entire set of candidate evaluation objects,<o i ,s j >is a candidate word pair, #<o i ,s j >For the frequency of occurrence of the candidate word pairs, sim: (<o i ,s j >) The relevance calculated for the relevance model.
Further, the combining the word mixing vector and the first candidate evaluation object set to obtain an accurate evaluation object from the second candidate evaluation object set, and combining the first candidate evaluation object set and the accurate evaluation object to obtain an accurate evaluation object set includes:
all candidate evaluation objects in the first candidate evaluation object set are used as accurate evaluation objects, and the accurate evaluation object set is input;
calculating the similarity of the word mixing vectors between the accurate evaluation object and the candidate evaluation objects of the second candidate evaluation object set;
and determining that the detected similarity is greater than a third threshold, taking the candidate evaluation object of the second candidate evaluation object set as an accurate evaluation object, and inputting the accurate evaluation object set.
Further, the method also comprises the following steps:
acquiring residual candidate evaluation objects from the word pair set, mining association rules of the residual candidate evaluation objects and the accurate evaluation object set to acquire association set pairs of evaluation relations, and inputting candidate evaluation objects which are not in the accurate evaluation object set into the accurate evaluation object set according to the association set pairs;
acquiring accurate candidate evaluation words from the candidate evaluation word set according to the word frequency, and acquiring low-frequency candidate evaluation objects from the word pair set according to the word frequency;
and calculating PMI-IR values of the low-frequency candidate evaluation objects and the accurate candidate evaluation words, determining that the detected PMI-IR value is greater than a fourth threshold value, and inputting the low-frequency candidate evaluation objects into an accurate evaluation object set.
The other technical scheme adopted by the invention is as follows:
an evaluation object recognition device for an electronic commerce product, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method described above.
The other technical scheme adopted by the invention is as follows:
a storage medium having stored therein processor-executable instructions for performing the method as described above when executed by a processor.
The invention has the beneficial effects that: in the recall strategy of the evaluation objects, the semantic similarity between the evaluation objects is measured by using the similarity of the word mixed vector, so that the recall rate and the accuracy of the evaluation objects are improved; in addition, the similarity of the word mixed vector can measure the semantic similarity between evaluation objects.
Drawings
Fig. 1 is a flowchart illustrating steps of an evaluation object recognition method for an electronic commerce product according to an embodiment of the present invention;
FIG. 2 is a diagram of a word-pair correlation model structure based on BERT-CNN according to an embodiment of the present invention;
fig. 3 is a flowchart of an iterative identification algorithm for evaluation elements according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings only for the convenience of description of the present invention and simplification of the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, a plurality of means is one or more, a plurality of means is two or more, and greater than, less than, more than, etc. are understood as excluding the essential numbers, and greater than, less than, etc. are understood as including the essential numbers. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
As shown in fig. 1, the present embodiment provides an evaluation object identification method for e-commerce products, which includes, but is not limited to, the following steps:
s1, obtaining a second comment corpus, and performing text preprocessing and labeling on the second comment corpus to obtain a training data set D.
The second comment corpus is a comment corpus of a type of electronic commerce products, such as a comment corpus of products in the cosmetic field or a comment corpus of an electric appliance, and the second comment corpus needs to be manually marked, so that the obtained amount is relatively small. In this embodiment, the labeled training data set D has a size of 5000, and specifically, the standard steps are as follows in steps S11 to S14:
s11, preprocessing the obtained comment sentences, wherein the preprocessing comprises the following steps: removing designated useless characters, performing word segmentation and part-of-speech tagging work on the sentences, and removing useless stop words after word segmentation. The initial single comment statement is P = c _1c _ … c _ i … c _ n, where c _ i represents the ith character in the comment statement, and after text preprocessing, the comment statement is represented as P = [ (w _ 1), (w _ 2), … (w _ i), …, (w _ m) ], where the participle and part-of-speech tagging tools may use the open source tool jieba.
And S12, manually marking the evaluation objects and the evaluation words in each sentence after the text preprocessing, and combining every two sentences to form word pairs.
S13, constructing sentence pairs of < original sentences and evaluation word pairs > based on the assumption that the core semantics of the comment sentences lie in evaluation collocation, manually marking whether the evaluation collocation relationship exists in the word pairs, if so, marking the class as 1, otherwise, marking the class as 0.
S14, a single sample of the data set D formed after labeling is < original sentence, and word pairs and corresponding part of speech sequences and categories of the original sentence after word segmentation are evaluated.
And S2, constructing a word pair association degree model, and training the word pair association degree model by adopting a training data set D.
In this embodiment, the word pair association model is a word pair association model based on BERT-CNN (BERT convolutional network), and BERT is an abbreviation of Bidirectional Encoder responses from transforms. The structure of the word pair association degree model based on BERT-CNN is shown in fig. 2, and specifically includes:
an input layer; the data of the input layer comprises a text series of comment sentences, a word pair series in the comment and a part of speech sequence after the comment is participated. The text series is [ c ] 1 ,c 2 ,…,c i ,…,c m ]Wherein c is i And represents the numerical value id corresponding to the ith character. The word pair series in the comment is p 1 ,p 2 ,…,p i ,…,p o ]Wherein p is i A numerical value id corresponding to the ith character in the word pair series. The part-of-speech sequence after comment participle is [ pos 1 ,pos 1 ,…,pos i ,…,pos n ]Wherein pos i A numerical value id corresponding to the ith character in the word pair series.
An embedding layer; the method comprises the following steps: the Chinese BERT-Base based on google has a built-in layer BERT Embedding (position Embedding, character Embedding and category Embedding) and an adjustable parameter Embedding layer POS Embedding.
A coding layer; the method comprises the following steps: a native programming Layer BERT Encoder Layer (12-Layer Transformer encoding Layer) for google-based chinese BERT-Base and a CNN-based encoding Layer for part-of-speech sequences.
A classification decoding layer; it is classified by the coding layer output vector CLS of BERT and the Pooling vector obtained by the part of speech sequence CNN coding layer output vector through Average Pooling, and the Concat combination is input to Softmax.
A training data set D is obtained through step S1, and the training data set D can be used to train a word pair association model. Training a word pair association degree model based on BERT-CNN, finely adjusting parameters of a BERT coding layer by using a training data set D in the training process, synchronously training the parameters of a part-of-speech sequence coding layer CNN by using the training data set D, finally using a cross entropy function as a target function of the model, and obtaining the required word pair association degree model after the training is finished.
S3, obtaining a first comment corpus, extracting candidate evaluation objects and candidate evaluation words from sentences of the first comment corpus according to preset parts of speech, pairwise combining the candidate evaluation objects and the candidate evaluation words in the same sentence to form word pairs, performing association calculation on the word pairs by adopting a trained word pair association model, and obtaining a word pair set CP according to a calculation result.
And additionally, a large amount of comment corpora (namely the first comment corpora) of the E-commerce products are obtained, and the comment corpora can be obtained in a large amount without being marked. And extracting candidate evaluation objects for each sentence in the corpus according to the part of speech v (verb part of speech) and n (noun part of speech), extracting candidate evaluation words according to the part of speech a (adjective part of speech) and n, and forming word pairs in pairs. Calculating the word pair relevance by using a BERT-CNN relevance model, and extracting the word pair relevance greater than a threshold value alpha 1 The word pairs form a word pair set CP; in this example α 1 ＝0.35。
And S4, performing iterative recognition on the word pair set by adopting a preset recognition algorithm to obtain a candidate evaluation object set CO and a candidate evaluation word set CS.
The preset identification algorithm is an evaluation element iterative identification algorithm, and specifically includes the following steps S41 to S45, referring to fig. 3:
s41, initializing a candidate evaluation object set CO to be empty, and initializing a candidate evaluation word set CS to be empty;
and S42, detecting each evaluation word in the word pair set CP, and if the evaluation word belongs to the seed emotion words, adding the evaluation words into the candidate evaluation word set CS. The seed emotion words are derived from a seed emotion dictionary, and data published by Hownet can be used.
S43, for each candidate evaluation object associated with the evaluation word in the candidate evaluation word set CS, calculating the association degree of the candidate evaluation object and the candidate evaluation word set CS, wherein the calculation formula is as follows:
wherein the content of the first and second substances,<o i ,s j >is a candidate word pair, #<o i ,s j >For the frequency of occurrence of the candidate word pairs, sim: (<o i ,s j >) The relevance calculated for the relevance model.
Taking the degree of correlation to be larger than a threshold value alpha 2 The evaluation object of (2) is added to the candidate evaluation object set CO. Where α is 2 ＝0.7。
S44, for each candidate evaluation word associated with the evaluation object in the candidate evaluation object set CO, calculating the association degree of the candidate evaluation word and the candidate evaluation object set CO, wherein the calculation formula is as follows:
wherein the content of the first and second substances,<o i ,s j >is a candidate word pair, #<o i ,s j >Frequency of occurrence of candidate word pairs, sim: (<o i ,s j >) The relevance calculated for the relevance model.
Taking the degree of correlation larger than a threshold value alpha 2 Is added to the candidate evaluation word set CS. Where α is 2 ＝0.7。
And S45, repeating the step S43 and the step S44 until the candidate evaluation object set CO and the candidate evaluation word set CS are not changed.
And S5, dividing the candidate evaluation objects in the candidate evaluation object set according to the word frequency to obtain a first candidate evaluation object set and a second candidate evaluation object set. Extracting word frequency larger than threshold lambda from candidate evaluation object set CO 1 The words are used as accurate evaluation objects (all candidate evaluation objects in the first candidate evaluation object set are the accurate evaluation objects), and the word frequency which is greater than the threshold lambda is extracted from the candidate evaluation word set CS 1 The term (2) is used as an accurate evaluation term. And the word frequency is the frequency of the candidate evaluation object or the candidate evaluation word appearing in the word pair set CP.
And S6, extracting the candidate evaluation objects remaining in the step S5 by utilizing a sequential combination mode of similarity calculation based on the word mixed vector, the association rule and the PMI to obtain a final accurate evaluation object set O.
The step S6 specifically includes steps S61 to S65:
s61, performing word segmentation and character segmentation on the large amount of comment linguistic data obtained in the step S3, performing word2vec training by using an open source generative tool, and obtaining a word vector and a character vector of each word respectively, wherein the form of the word vector is [ w [ ] i1 ,w i2 ,…,w ik ,…,w im ]Wherein w is ik Is the value of the k-th dimension of the word vector of the ith word, and m is the dimension of the word vector; the word vector is of the form [ c ] i1 ,c i2 ,…,c ik ,…,c im ]Wherein c is ik Is the value of the k-th dimension of the word vector of the ith word, and m is the dimension of the word vector. Here m =50.
S62, combining the word vectors into a word mixed vector based on the word vectors obtained by training in the step S71, wherein the combination mode is as follows:
wherein o represents an evaluation object, c represents a word constituting o, w' 0 Representing a word-blending vector, w o Representing a word vector, c i Representing a word vector.
S63, traversing the remaining candidate evaluation objects in the step S5, and based on the word mixed vector combination mode in the step S62, enabling the similarity of the word mixed vector of the candidate evaluation objects and the word mixed vector of the accurate evaluation object to be larger than a threshold lambda 2 The evaluation object of (2) is added to the accurate evaluation object set O. Said threshold lambda 2 The value range of (A) is as follows: lambda [ alpha ] 2 ∈(0,1]Here, take λ 2 ＝0.7。
And S64, carrying out association rule discovery on the remaining candidate evaluation objects and the accurate evaluation object set O obtained in the step S63 to obtain an association set pair of the evaluation relation, and adding the words which are not in the set O into the accurate evaluation object set O.
The remaining candidate evaluation objects are the word pair set CP obtained in step S3, and after the candidate evaluation objects are extracted in step S63, the remaining candidate evaluation objects are obtained.
S65, calculating a PMI-IR value of the word pair containing the low-frequency candidate evaluation object and the accurate evaluation word by utilizing the search engine, and enabling the PMI-IR value to be larger than a threshold value lambda 3 The evaluation object in the word pair of (1) is added to the accurate evaluation object set O.
The threshold value lambda 3 The value range is as follows: lambda 3 ∈(-∞,0]Here, take λ 3 And (4) = -6. The low-frequency candidate evaluation object is the word pair set CP obtained in step S3, and after the candidate evaluation objects are extracted in steps S63 to S64, the remaining candidate evaluation objects are obtained.
The PMI-IR formula is:
where hit (x) is the number of hits of the search term x in the search engine, and ε is a constant term.
In summary, compared with the prior art, the evaluation object identification method for the e-commerce product of the embodiment has the following advantages and beneficial effects:
1. in the embodiment, a technical scheme that a deep learning model based on BERT-CNN is combined with a two-way propagation evaluation object recognition is adopted, specifically, a candidate word pair with an evaluation relation in each sentence is extracted, and an evaluation object and an evaluation word are iteratively recognized from emotion words.
2. According to the embodiment, a word pair association degree extraction model based on BERT-CNN is trained, a large-scale pre-training model is utilized for model fine adjustment, a good effect can be achieved only by marking a small amount of corpora, marking cost is saved, and semantic information of comment sentences and part-of-speech information of sentence word sequences are considered.
3. In the evaluation object recall strategy, the semantic similarity between the evaluation objects is measured by using the word mixed vector similarity, so that error continuation of wrong division of a word segmentation tool is reduced and the recall rate and accuracy of the evaluation objects are improved compared with the method of calculating the semantic similarity between the evaluation objects by using only the word vector similarity.
4. In the embodiment, a plurality of evaluation object recall strategies based on similarity calculation of word mixed vectors, association rules and PMI sequential combination mode are adopted, wherein the word mixed vector similarity measures semantic similarity between evaluation objects, the association rules measure association of the evaluation objects in linguistic data, and the PMI-IR measures association degree between the evaluation objects and evaluation words based on a search engine, so that the problem of long-tailed word statistical failure caused by insufficient linguistic data is solved.
The embodiment also provides an evaluation object recognition device for e-commerce products, which comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method described above.
The evaluation object identification device for the e-commerce product can execute the evaluation object identification method for the e-commerce product provided by the method embodiment of the invention, can execute any combination implementation steps of the method embodiment, and has corresponding functions and beneficial effects of the method.
The present embodiments also provide a storage medium having stored therein processor-executable instructions, which when executed by a processor, are configured to perform the method as described above.
The storage medium of the embodiment can execute the evaluation object identification method facing the e-commerce product provided by the method embodiment of the invention, can execute any combination of the implementation steps of the method embodiment, and has corresponding functions and beneficial effects of the method.
It will be understood that all or some of the steps, systems of methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (8)
1. An evaluation object identification method for E-commerce products is characterized by comprising the following steps:
acquiring a first comment corpus, extracting candidate evaluation objects and candidate evaluation words from sentences of the first comment corpus according to preset parts of speech, and pairwise forming word pairs by the candidate evaluation objects and the candidate evaluation words in the same sentence;
performing association degree calculation on the word pairs by adopting the trained word pair association degree model, and obtaining a word pair set according to a calculation result;
performing iterative recognition on the word pair set by adopting a preset recognition algorithm to obtain a candidate evaluation object set and a candidate evaluation word set;
dividing candidate evaluation objects in the candidate evaluation object set according to word frequency to obtain a first candidate evaluation object set and a second candidate evaluation object set, wherein the word frequency is the frequency of the candidate evaluation objects or the candidate evaluation words appearing in the word pair set;
combining a word mixed vector and the first candidate evaluation object set, obtaining an accurate evaluation object from the second candidate evaluation object set, and combining the first candidate evaluation object set and the accurate evaluation object to obtain an accurate evaluation object set, wherein the word mixed vector is a combination of a word vector and a word vector;
the preset recognition algorithm comprises a first formula and a second formula, the iterative recognition is carried out on the word pair set by adopting the preset recognition algorithm to obtain a candidate evaluation object set and a candidate evaluation word set, and the method comprises the following steps:
initializing a candidate evaluation object set and a candidate evaluation word set to be null;
acquiring an evaluation word from the word pair set, determining that the evaluation word belongs to the seed emotion word, and inputting the evaluation word into the candidate evaluation word set;
acquiring a candidate evaluation object from the word pair set, calculating a first association degree by combining the candidate evaluation object, the candidate evaluation word set and a first formula, determining that the first association degree is greater than a first threshold value, and inputting the candidate evaluation object into the candidate evaluation object set;
acquiring candidate evaluation words from the word pair set, calculating a second association degree by combining the candidate evaluation words, the candidate evaluation object set and a second formula, determining that the second association degree is greater than a second threshold value, and inputting the candidate evaluation words into the candidate evaluation word set;
the first formula is:
wherein rel i As candidate evaluation object o i Degree of association with the entire set of candidate evaluation words, < o i ，s j Is more than candidate word pair, # < o i ，s j Is the frequency of occurrence of the candidate word pair, sim (< o) i ，s j >) relevance calculated for the relevance model;
the second formula is:
wherein rel j As candidate evaluation words s j Degree of association with the entire set of candidate evaluation objects, < o i ，s j Is more than candidate word pair, # < o i ，s j Is the frequency of occurrence of the candidate word pair, sim (< o) i ，s j >) is the degree of association calculated by the association model.
2. The method for identifying the evaluation object oriented to the e-commerce product according to claim 1, further comprising a step of training a word pair association degree model, specifically:
acquiring a second comment corpus, and performing text preprocessing and labeling on the second comment corpus to obtain a training data set;
and constructing a word pair association degree model, and training the word pair association degree model by adopting a training data set.
3. The method for identifying the evaluation object facing the e-commerce product, according to claim 2, wherein the obtaining of the second comment corpus, the text preprocessing and labeling of the second comment corpus, and the obtaining of the training data set comprise:
acquiring a second comment corpus, and preprocessing the second comment corpus to obtain a plurality of comment sentences, wherein the preprocessing comprises word segmentation and part-of-speech tagging of the sentences in the second comment corpus;
marking out an evaluation object and an evaluation word in a comment sentence in a manual mode, and combining the evaluation object and the evaluation word in pairs to serve as a word pair;
constructing sentence pairs less than the original sentences and word pairs, marking whether the evaluation collocation relationship exists in the word pairs or not, and marking the class as a first type if the evaluation collocation relationship exists; otherwise, the class is marked as a second type;
and forming a training data set by the marked comment sentences, wherein the format of the marked comment sentences is < original sentences, and evaluating word pairs, and the corresponding part-of-speech sequence and category after the original sentences are segmented.
4. The method as claimed in claim 3, wherein the word pair association model is a BERT-CNN-based word pair association model, the word pair association model includes an input layer, an embedding layer, a coding layer and a classification decoding layer, and the training of the word pair association model using the training data set includes:
fine-tuning parameters of the coding layer by adopting a training data set;
and training the word pair association degree model by adopting a cross entropy function as a target function to obtain a final word pair association degree model.
5. The method for identifying evaluation objects oriented to electronic commerce products of claim 1, wherein the combining the word mixing vector and the first candidate evaluation object set, obtaining accurate evaluation objects from the second candidate evaluation object set, and combining the first candidate evaluation object set and the accurate evaluation objects to obtain the accurate evaluation object set comprises:
all candidate evaluation objects in the first candidate evaluation object set are used as accurate evaluation objects, and the accurate evaluation object set is input;
calculating the similarity of the word mixing vectors between the accurate evaluation object and the candidate evaluation objects of the second candidate evaluation object set;
and determining that the detected similarity is greater than a third threshold, taking the candidate evaluation object of the second candidate evaluation object set as an accurate evaluation object, and inputting the accurate evaluation object set.
6. The method for identifying the evaluation object facing the electronic commerce product according to claim 5, further comprising the steps of:
acquiring residual candidate evaluation objects from the word pair set, mining association rules of the residual candidate evaluation objects and the accurate evaluation object set to acquire association set pairs of evaluation relations, and inputting candidate evaluation objects which are not in the accurate evaluation object set into the accurate evaluation object set according to the association set pairs;
acquiring accurate candidate evaluation words from the candidate evaluation word set according to the word frequency, and acquiring low-frequency candidate evaluation objects from the word pair set according to the word frequency;
and calculating PMI-IR values of the low-frequency candidate evaluation objects and the accurate candidate evaluation words, determining that the detected PMI-IR value is greater than a fourth threshold value, and inputting the low-frequency candidate evaluation objects into an accurate evaluation object set.
7. An evaluation object recognition device for an electronic commerce product, comprising:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor may implement the method for identifying an evaluation object for an electronic commerce product according to any one of claims 1 to 6.
8. A storage medium having stored therein processor-executable instructions, which when executed by a processor, are configured to perform the method of any one of claims 1-6.
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